Processing of Sociological Information

Total Hours in Course81

Number of hours for lectures12

Number of hours for seminars and practical classes12

Independent study hours57

Date of course confirmation04.11.2020

Responsible UnitInstitute of Social Sciences and Humanities

Course developer

lect.

Lana Janmere

Mg. sc. soc.

Prior knowledge

Soci5041, Social Research Methods

Course abstract

The aim of the study course is to provide in-depth theoretical knowledge and practical skills in the processing and analysis of sociological research data required for further professional activities in public administration, research organizations and any private sector organization engaged in the study and quantitative measurement of human behavior and views. The study course provides in-depth knowledge and skills in the processing of sociological research results, which allow them to be correctly analysed and interpreted. Master students both theoretically and practically learn the most important descriptive and inferential statistical methods and their application in various designs of quantitative research, in which a survey is used as a data acquisition method. Master students gain an idea of the meaning of data-based conclusions, their making and interpretation, which allows to test existing and develop new knowledge in sociological theory. The practical classes of the study course are focused on the application of specific data processing methods in the collection and analysis of survey results. The study course promotes the development of critical thinking and personal competencies.

Learning outcomes and their assessment

Knowledge: The student knows and understands the essence of quantitative data processing and analysis in various quantitative research designs, knows the most suitable statistical processing methods for the survey data, the conditions of their use – practical works, exam.
Professional skills: Able to independently create a research database and test it, able to collect survey data, obtain primary results and display them graphically, able to measure the closeness of the survey data, determine their statistical significance and compare group mean trends – practical works.
Soft skills: Ability to plan tasks responsibly, able to cooperate, engage in discussion and reasonably defend one's point of view, able to critically evaluate the obtained information and draw data-based conclusions - practical works, exam, discussions.
Competence: Able to independently plan and implement quantitative data processing, analysis and drawing conclusions to gain new knowledge about the research problem, able to independently solve non-standard situations in the implementation of sociological research and the selection of appropriate data processing methods – exam.

Course Content(Calendar)

1.Methods of obtaining quantitative data (1 hour).
2.An overview of the most popular computer programs used to process survey data. Data file preparation in IBM SPSS (1 hour).
3.Obtaining primary results for analyzing social tendencies in sociological researches (1 hour).
4.Descriptive study design. Interpretation of primary results in a descriptive study (1 hour).
5.Indicators for measuring the closeness of data relationships in analysis of social problems (2 hours).
6.Determining the statistical significance of the detected relationships in the data (2 hours).
7.Assessing group differences and comparing average trends (2 hours).
8.Quasi-experimental study design. Definition of independent and dependent variables, determination of their interaction using simple linear regression analysis (2 hours).

List of practical work:
1. Preparation of the research base, input and verification of survey data (3 hours).
2. Acquisition of primary results, their interpretation and reflection in graphic solutions (4 hours).
3. Measuring the closeness of data relationships, determining statistical significance and comparing average trends. Interpretation of the obtained results (5 hours).

Part-time studies:
All topics specified for full time studies are accomplished, but the number of contact hours is one half of the number specified in the calendar.

Requirements for awarding credit points

3 practical works must be elaborated. Written exam at the end of the course.

Study process in part-time distance learning is organised in accordance with the Order of the Vice-Rector of Studies No. 2.4.-5/59 On distance learning procedures at LBTU. Study courses are scheduled for each semester according to the study plan. The students learn the topics included in the study course independently, using the materials created and placed by the lecturer in the e-studies (Moodle). Feedback on the learning of lecture and seminar topics in distance learning is organised in the form of self-assessment tests, discussion forums and independent work, as well as in face-to-face or online consultations, lectures and final examinations according to the timetable.

Description of the organization and tasks of students’ independent work

1. Practical work: Preparation of the research base, input and verification of survey data.
2. Practical work: Acquisition of primary results, their interpretation and reflection in graphic solutions.
3. Practical work: Measuring the closeness of data relationships, determining statistical significance and comparing average trends. Interpretation of the obtained results.
4. Written exam at the end of the course.

Criteria for Evaluating Learning Outcomes

Exam. Its evaluation consists of: practical work 1 (10%), practical work 2 (20%), practical work 3 (30%), exam (40%).

Compulsory reading

1. Bryan F.J. Manly, Jorge A.Navarro Alberto (2016). Multivariate Statistical Methods: A Primer. Fourth Edition, 269 p. Pieejama kā e-grāmata ESAF Informācijas kabinetā.
2. Bourke, J., Kirby, A., Doran, J. (2016). Survey & questionnaire design: Collecting Primary Data to Answer Research Questions: eBook. Ireland: NuBooks, p. 47. Retrieved from EBSCO eBook Academic Collection via LLU Fundamental library network.
3. Data Science & Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data (2015). Indianapolis: John Willey & Sons, 410 p. ISBN: 978-1-118-87613-8
4. Elst van H. (2019) Foundations of Descriptive and Inferential Statistics. Germany, p. 176. Elektroniski pieejama https://arxiv.org/pdf/1302.2525.pdf.
5. Jansons V., Kozlovskis K. (2015). Mārketinga pētījumi: teorija un prakse SPSS 20 vidē. 1. daļa. Rīga, RTU izdevniecība, 400 lpp. Tiešsaistē pieejama https://dom.lndb.lv/data/obj/841728.html.
6. Miller J. (2022) Making Sense of Numbers: Quantitative Reasoning for Social Research. Thousand Oaks, California: Sage Publications Inc, 569 p. ISBN: 978-1-5443-5559-7.

Periodicals and other sources

1. European Societies - European Sociological Association, UK. Pieejams: http://www.europeansociology.org/index.php?option=com_content&task=view&id=48&Itemid=29
2. International Journal of Social Research Methodology. Pieejams: http://www.tandfonline.com/loi/tsrm20
3. R Programming Tutorial - Learn the Basics of Statistical Computing. Pieejams: https://www.youtube.com/watch?v=_V8eKsto3Ug

4. Statistics - A Full University Course on Data Science Basics. Pieejams: https://www.youtube.com/watch?v=xxpc-HPKN28

Notes

Mandatory study course in the ESAF academic master's study program "Sociology of Organizations and Public Administration".

Atsauksmes